Assess convergence of a runjags model

Source: R/JAGS-fit.R

Checks whether the supplied runjags-package model satisfied convergence criteria.

JAGS_check_convergence(
  fit,
  prior_list,
  max_Rhat = 1.05,
  min_ESS = 500,
  max_error = 0.01,
  max_SD_error = 0.05,
  add_parameters = NULL,
  fail_fast = FALSE
)

Arguments

fit

a runjags model

prior_list

named list of prior distribution (names correspond to the parameter names)

max_Rhat

maximum R-hat error for the autofit function. Defaults to 1.05.

min_ESS

minimum effective sample size. Defaults to 500.

max_error

maximum MCMC error. Defaults to 1.01.

max_SD_error

maximum MCMC error as the proportion of standard deviation of the parameters. Defaults to 0.05.

add_parameters

vector of additional parameter names that should be used (only allows removing last, fixed, omega element if omega is tracked manually).

fail_fast

whether the function should stop after the first failed convergence check.

Value

JAGS_check_convergence returns a boolean indicating whether the model converged or not, with an attribute 'errors' carrying the failed convergence checks (if any).

See also

JAGS_fit()

Examples

if (FALSE) { # \dontrun{
# simulate data
set.seed(1)
data <- list(
  x = rnorm(10),
  N = 10
)
data$x

# define priors
priors_list <- list(mu = prior("normal", list(0, 1)))

# define likelihood for the data
model_syntax <-
  "model{
    for(i in 1:N){
      x[i] ~ dnorm(mu, 1)
    }
  }"

# fit the models
fit <- JAGS_fit(model_syntax, data, priors_list)
JAGS_check_convergence(fit, priors_list)
} # }